Authenticity testing of documents is generally done by measuring certain authenticity features, for example optical, electric or magnetic features, on a document under test and then testing the measured authenticity features with reference to given authenticity criteria. For example, the optical reflection behavior of the document is measured as an authenticity feature and it is then tested whether the measured reflection behavior undershoots or exceeds a certain threshold value as the associated authenticity criterion. Depending on the test result the document is classified as authentic or false.
The reliability of detecting forgeries can be increased for instance by tightening the authenticity criteria in the testing of certain authenticity features, for example by raising or lowering threshold values. In practice the authenticity criteria cannot be tightened at will, however, since this would make the proportion of authentic documents not recognized as authentic—and possibly rejected or misclassified—too high.
In bank note processing machines that are used in particular in commercial banks for deposit testing and clearing, this would lead for example to elevated effort for postprocessing bank notes not recognized as authentic by hand and possibly further by machine.
In authenticity testing in money-depositing machines, a general tightening of authenticity criteria would mean that in particular used or soiled authentic bank notes, whose authenticity features are less distinct due to soiling or damage compared to freshly printed bank notes, are not recognized as authentic and consequently rejected or withheld as alleged forgeries, depending on the case of application.
The reliability in recognizing counterfeit bank notes is therefore limited by the required low proportion of authentic bank notes not recognized as authentic. This is problematic especially when forgeries are not recognized as such due to “loose” authenticity criteria and return to circulation, for example after one customer deposits counterfeit bank notes in self-service recycling machines and the bank notes not identified as forgeries are then issued to other customers.
The method known from DE 196 18 541 A1 relates to determining a sorting class from a number of bank note properties, such as denomination, security features and soiling. Measuring results for the bank note properties are first mapped onto discrete classes and combined in a class vector. The class vector is finally compared with individual rule vectors each corresponding to a certain sorting class. If the class vector of the bank note matches a rule vector, the bank note is assigned the sorting class corresponding to the particular rule vector. This method permits sorting classes to be determined fast and precisely. However, the derivation of a class for individual security features, i.e. the actual authenticity testing, is done by methods known from the prior art, so that the above-described problems also arise here when for example a raising or lowering of threshold values for authenticity features is intended to increase or reduce the reliability in authenticity testing.
EP 0 101 115 A1 discloses a device for recognizing bank notes wherein a digital picture of the bank note is taken and compared with a previously stored reference picture of a reference bank note. If a first comparison, in particular on one half of the bank note, does not yield a sufficiently reliable result, the comparison can be repeated in other areas of the bank note, for example with other comparative values. However, this opens up the possibility of selectively soiling or damaging security-relevant areas of a counterfeit bank note to effect a test of other areas with possibly more easily imitated security features and thus—falsely—a positive test result.